The LeanScale Podcast · Episode 98

AI-Native GTM: 3 Agent Plays and the Layer That Makes Them True

LeanScale CTO Jake Toepel runs an agent through ICP, messaging and pipeline diagnosis — then shows why it breaks on your own CRM

Jake Toepel · Chief Technology Officer · LeanScale Presented by Jake Toepel
Published Updated 00:07:24 6 min read 1265 words
AI in GTM Revenue Operations GTM Strategy Forecasting
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Executive Summary

The one-paragraph brief, extended

Why this conversation matters — and who should spend the hour.

Boards are asking for an AI-native go-to-market and almost nobody has been told what the phrase means. Jake Toepel, LeanScale's CTO, opens this short demonstration with a working definition rather than a slide: agents running the work that used to be non-humanly possible — the analysis nobody had time for, the answer that took a data team three sprints — on demand, in plain English. He then runs one agent through three of the highest-leverage plays in go-to-market before deliberately breaking it.

The first play asks the uncomfortable version of the ICP question. Every company has an ICP slide written eighteen months ago by someone who no longer works there. Jake instead asks the agent to take every account closed in the last four quarters and cross-reference deal size, sales cycle and six-month retention: which segments actually win, and which does the company keep selling to that it should not? The answer is that the real ICP is not the enterprise logos on the website but mid-market with a strong technical champion, closing in half the time — a board-level conversation that used to take a quarter, had in ninety seconds.

The second play tests whether messaging lands. Marketing has positioning, the market has opinions, and those are usually not the same document. Handing the agent the last twenty sales calls plus current messaging returns receipts: "implementation in weeks, not months" closes deals and should be kept; "one platform instead of five tools" should be killed, because prospects hear jack of all trades, master of none. The third play is a live pipeline diagnostic inside the Monday forecast call — five whys down to connect rates collapsing six weeks earlier, exactly when two reps ramped down. Root cause before the call ends, and Jake argues that speed is the actual moat.

Then he does the part nobody demos. Pointed at a real CRM and asked for pipeline coverage ratio, raw AI returns a number instantly, confidently, and wrong. Four things are missing: shared definitions, so expansion and new business pipeline get blended; identity resolution, so billing saying churned and Salesforce saying open cannot be reconciled and Acme Inc, Acme Co and Acme 123 are unknowable; the plan, meaning targets and quota to measure against; and memory of what an ICP means here. Without those, every answer is a confident guess — worse than no answer, because it ends up in front of the board.

The fix is the context graph, which Jake calls the single most important part of AI-native GTM and the piece almost nobody discusses: a semantic layer mapping raw data to what it means in the business and resolving every record to one identity across CRM, billing and product. LeanScale builds it on Vasco. With it, the agent reads off a model the team has already agreed is correct — the difference between a demo that impresses and a system worth betting a quarter on. It is also why buying the whole team licences without it fails: everyone invents their own definition of pipeline and you get fifty versions of the truth. His closing advice is that the place to start is not more tools but an honest look at whether the data can support this yet.

Key Takeaways

14 things worth stealing

The load-bearing ideas, each with the business implication and who should care.

01

AI-native GTM has a working definition: agents doing what was not humanly possible

Not a chatbot bolted onto the CRM. Jake's definition is agents running the analysis nobody had time for and the answers that used to take a data team three sprints, available on demand in plain English.

Why it matters: Judge an AI-native claim by whether it produces analysis the team genuinely could not do before, rather than by whether there is a chat box in the product.

Revenue ExecutivesRevOps LeadersFounders
02

The ICP question worth asking is the uncomfortable one

Not "who is our ICP" but: across every account closed in the last four quarters, cross-referencing deal size, sales cycle and six-month retention, which segments actually win and which do we keep selling to that we should not?

Why it matters: Frame the query so it can contradict you. An ICP question that cannot return an unwelcome answer will confirm the slide you already have.

FoundersRevenue ExecutivesMarketing Leaders
03

The ICP slide is usually stale and usually orphaned

Jake's characterisation is that it was written eighteen months ago by somebody who does not work at the company any more — and in the demo the real ICP turns out to be mid-market with a strong technical champion, not the enterprise logos on the website.

Why it matters: Re-derive the ICP from closed-won evidence on a cadence rather than treating it as a fixed artefact, and check whether the logos on the site match the segment that actually retains.

FoundersMarketing LeadersRevenue Executives
04

Retention belongs in the ICP definition, not just deal size and cycle

The winning segment is identified by cross-referencing six-month retention alongside deal size and sales cycle — which is what separates a segment that closes from a segment that stays.

Why it matters: An ICP defined on acquisition metrics alone will point at segments that buy easily and leave. Include a retention window in the definition.

Revenue ExecutivesCustomer SuccessFounders
05

A messaging teardown against real calls returns receipts, not opinions

Handing an agent the last twenty sales calls plus current messaging surfaces which lines land and which fall flat, quoted from the calls themselves. "Implementation in weeks, not months" is a keeper; "one platform instead of five tools" backfires because prospects hear jack of all trades, master of none.

Why it matters: Replace the six-week focus group with a weekly teardown against calls you already record. The quotes are what make the recommendation arguable rather than a matter of taste.

Marketing LeadersSales LeadersFounders
06

Positioning and market perception are usually different documents

Marketing has positioning, the market has opinions, and Jake's point is that these routinely diverge without anyone noticing because nothing systematically compares them.

Why it matters: Treat the gap between stated positioning and recorded buyer language as a measurable quantity with an owner, not as an unavoidable fact of marketing.

Marketing LeadersFounders
07

Five-whys root cause can happen inside the forecast call

Pipeline down quarter over quarter, concentrated in one region, in outbound specifically, because connect rates fell off a cliff six weeks ago when two reps ramped down. Normally that chain is a three-week data-team round trip returning when half the quarter is gone.

Why it matters: Move diagnosis into the meeting where the decision is made. An answer that arrives after the quarter has turned is an explanation, not a decision input.

Revenue ExecutivesSales LeadersRevOps Leaders
08

Speed is the moat — constant recalibration rather than late discovery

Jake's framing is that the unlock is not headcount or tooling but the ability to recalibrate continuously instead of finding out you were wrong a quarter too late.

Why it matters: Measure the analytics function on time-to-answer during the period, not on the quality of the retrospective. The value decays with the quarter.

Revenue ExecutivesRevOps LeadersFounders
09

Raw AI on a real CRM returns a confident, instant, wrong answer

Asked for pipeline coverage ratio, it produces a number immediately with no signal that it is unreliable. This is the failure the demo never shows.

Why it matters: Test any AI-on-CRM claim with a question that has a known correct answer before trusting it on one that does not. Speed and confidence are not evidence of correctness.

RevOps LeadersRevenue ExecutivesFounders
10

Four things are missing: definitions, identity, plan, memory

Shared definitions — expansion and new business pipeline get blended because the model does not know they differ. Identity resolution — billing says churned, Salesforce says open, and Acme Inc versus Acme Co versus Acme 123 is unknowable. The plan — targets and quota to measure against. Memory — what an ICP means here and what happened last quarter.

Why it matters: Use the four as a readiness checklist before deploying agents against revenue data. Each one that is absent produces a specific, predictable class of wrong answer.

RevOps LeadersRevenue Executives
11

A confident guess is worse than no answer

Jake's reasoning is about where the answer ends up: an unreliable number that arrives fast and sounds certain gets carried into the board meeting, which is worse than having nothing to present.

Why it matters: Design agent surfaces to refuse rather than approximate when the underlying context is missing. Silence is recoverable; a confident wrong number that reached the board is not.

Revenue ExecutivesFoundersRevOps Leaders
12

The context graph is the semantic layer that makes agent answers trustworthy

It maps raw data to what it means in the business — definitions, motions, plan — and resolves every record to a single source of truth across CRM, billing and product. LeanScale builds it on Vasco. With it, the agent reads off a model the team has already agreed is correct.

Why it matters: The layer is the prerequisite, not an optimisation. It is the difference between a demo that impresses and a system worth betting a quarter on.

RevOps LeadersRevenue ExecutivesFounders
13

Licences without the layer produce fifty versions of the truth

Buying the whole team AI licences without a shared semantic layer means everyone invents their own definition of pipeline, and the organisation ends up with as many answers as it has people asking.

Why it matters: Sequence the spend: shared definitions first, seats second. Distributing seats onto un-normalised data multiplies disagreement rather than access.

Revenue ExecutivesFoundersRevOps Leaders
14

Start with an honest look at the data, not with more tools

Jake's closing instruction to teams under pressure to deliver AI-native GTM is that the first step is assessing whether the data can support it at all.

Why it matters: Run a readiness assessment before a procurement cycle. Buying tooling ahead of the foundation front-loads the cost and defers the failure.

FoundersRevenue ExecutivesRevOps Leaders
Frameworks Discussed

5 named models

Every framework Jimmy names, defined and time-stamped.

AI-Native GTM (working definition)

01:07

Agents running the work that used to be non-humanly possible — the analysis nobody had time for and the answers that took a data team three sprints — on demand, in plain English.

Offered explicitly as a replacement for the slide-deck version. The test is whether the work was previously impossible rather than merely manual, which is what separates it from a chatbot on top of a CRM.

The Three Agent Plays

01:31

ICP analysis cross-referencing deal size, sales cycle and six-month retention; a messaging teardown against recorded sales calls; and a live pipeline diagnostic run inside the forecast meeting.

Chosen as the highest-leverage go-to-market questions an agent can answer, and demonstrated in less time than booking the meeting to discuss them would take.

The Four Missing Things

04:43

Shared definitions, identity resolution, the plan, and memory — the four absences that make raw AI on a CRM return confident wrong answers.

Each maps to a specific failure: blended pipeline types, unresolved duplicate accounts, no target to measure against, and no company-specific context. Together they are a readiness checklist.

The Context Graph

05:46

A semantic layer beneath the agent that maps raw data to what it means in the business — definitions, motions, plan — and resolves every record to a single identity across CRM, billing and product.

Jake calls it the single most important part of AI-native GTM and the piece almost nobody discusses. LeanScale builds it on Vasco. It is what turns an agent from a guesser into something reading off an agreed model of the business.

Speed Is the New Moat

04:04

Competitive advantage comes from constant recalibration during the quarter rather than from headcount or tooling — discovering you were wrong while it can still be changed.

The conclusion drawn from the live diagnostic: the same root-cause analysis delivered three weeks later, with half the quarter gone, is an explanation rather than a decision.

Best Quotes

22 lines worth clipping

Pulled verbatim. Copy or share any of them.

“Right now, there's a good chance your CEO or your board has told you they want an AI-native go-to-market. And there's an even better chance nobody's actually told you what that means.”
Jake Toepel 00:00
“It is the single most important part of AI-native GTM, and almost nobody is talking about it.”
Jake Toepel 00:38
“Not slide decks about AI, the actual working systems.”
Jake Toepel 01:03
“AI-native GTM just means this, agents running the work that used to be non-humanly possible.”
Jake Toepel 01:10
“Every company has an ICP slide. It was written 18 months ago by somebody who does not work here anymore.”
Jake Toepel 01:35
“Tell me which segments actually win and which ones we keep selling to that we should not.”
Jake Toepel 01:52
“Our real ICP is not the enterprise logos on the website.”
Jake Toepel 02:09
“That is a board level strategy conversation that used to take a quarter. We just had it in 90 seconds.”
Jake Toepel 02:19
“Marketing has positioning. The market has opinions. Those are usually not the same document.”
Jake Toepel 02:29
“One platform instead of five tools. Kill it. Prospects hear that as a jack of all trades, master of none.”
Jake Toepel 02:57
“That is your real voice of customer on demand every single week if you want it.”
Jake Toepel 03:07
“Five wise, a root cause in the meeting, the thing that used to take three sprints and a dashboard request just happened before the call ended, and that is the real unlock speed.”
Jake Toepel 03:57
“Speed is the new moat and go to market constant recalibration instead of finding out you were wrong a quarter too late.”
Jake Toepel 04:09
“Wow, it gave us a number instantly, confidently, and it's wrong.”
Jake Toepel 04:33
“Is Acme Inc, Acme Co, Acme 123 the same company? The A.I. has no idea.”
Jake Toepel 05:04
“Without those four, every answer is a confident guess and a confident guess is worse than no answer because you're going to put that in front of the board.”
Jake Toepel 05:27
“Underneath the agent sits what we call the context graph, a semantic layer that maps your raw data to what it actually means in your business.”
Jake Toepel 05:46
“When the agent answers now, it's not just guessing, it's reading off a model of your business that you have already agreed is correct.”
Jake Toepel 06:08
“That is the difference between a demo that wows you and a system you would bet the quarter on.”
Jake Toepel 06:17
“Everyone invents their own definition of pipeline. Now you have 50 versions of the truth.”
Jake Toepel 06:29
“It's not a chatbot bolted onto your CRM. It's agents running real plays on a foundation that makes the answers true.”
Jake Toepel 06:43
“The place to start is not buying more tools. It's an honest look at whether your data can even support this yet.”
Jake Toepel 06:51
Practical Advice

What should you actually do?

The playbook, split by the seat you sit in.

RevOps Leaders

  • Audit against the four missing things before deploying any agent on revenue data: shared definitions, identity resolution, the plan, and memory. Each absence produces a predictable class of wrong answer.
  • Test an AI-on-CRM setup with a question whose correct answer you already know. Speed and confidence in the response carry no information about accuracy.
  • Resolve identity across CRM, billing and product first — an agent cannot tell whether Acme Inc, Acme Co and Acme 123 are one company, and will silently treat them as three.
  • Build the semantic layer once rather than distributing seats onto un-normalised data, which multiplies disagreement rather than access.
  • Design agent surfaces to decline when context is missing rather than approximate. A confident wrong number that reaches the board cannot be walked back.

Revenue Executives

  • Move root-cause diagnosis into the forecast call itself. An answer that lands three weeks later, half a quarter gone, is an explanation rather than a decision input.
  • Judge an AI-native claim by whether it produces analysis the team could not previously do, not by the presence of a chat interface.
  • Sequence the investment: shared definitions before licences. Fifty seats without a common definition of pipeline gives fifty answers.
  • Start with a data-readiness assessment rather than a procurement cycle.

Marketing Leaders

  • Run a messaging teardown against recorded calls weekly instead of commissioning a focus group. The quoted buyer language is what makes the recommendation arguable.
  • Treat the gap between stated positioning and recorded buyer language as a measurable quantity with an owner.
  • Check whether the logos on the website match the segment that actually retains — in the demo they did not.

Founders

  • Frame the ICP question so it can contradict you, and include a retention window alongside deal size and sales cycle.
  • Re-derive the ICP from closed-won evidence on a cadence rather than treating the slide as a durable artefact.
  • Take the closing instruction literally: the first step is an honest look at whether the data can support this, not more tooling.
AI Takeaways

How AI actually changes GTM

LeanScale's signature read on the AI-in-GTM question this episode wrestles with.

The thesis

The demo is not the hard part and Jake says so by breaking it on purpose. Three agent plays run convincingly on prepared ground; the same agent pointed at a real CRM returns a confident wrong number in seconds. The differentiator in AI-native GTM is the semantic layer underneath — definitions, identity, plan and memory — not the model or the interface.

Agent & automation ideas

  • An ICP agent cross-referencing deal size, sales cycle and six-month retention across closed-won accounts on a standing cadence.
  • A weekly messaging teardown against recorded calls, returning keep/kill recommendations with buyer quotes attached.
  • A forecast-call diagnostic that chains five whys to a root cause live, rather than filing a dashboard request.
  • An identity-resolution agent reconciling account records across CRM, billing and product before any analysis runs.
  • A refusal layer that declines to answer when a required definition or target is missing, instead of approximating.
Operations Takeaways

By function

The same conversation, filtered for RevOps, pipeline/marketing ops, and customer ops.

Revenue Operations

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Pipeline & Marketing Ops

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Customer Operations

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Metrics Mentioned

The numbers, with context

90 seconds
Time to an ICP answer

The board-level segmentation conversation that Jake says previously took a quarter, run live by the agent in the demonstration.

Four quarters of closed accounts
Accounts analysed for the ICP play

Cross-referenced on deal size, sales cycle and six-month retention to identify which segments actually win.

20
Sales calls used in the messaging teardown

Handed to the agent alongside current messaging to surface which lines land and which fall flat, quoted from the calls.

~3 weeks / three sprints
Traditional turnaround for a pipeline root cause

The data-team round trip the live diagnostic replaces — by which point Jake notes half the quarter is gone.

50
Versions of the truth without a semantic layer

Jake's characterisation of what happens when a team is given AI licences but no shared definition of pipeline.

Entities

Companies, people & tools mentioned

Auto-extracted and linked into the knowledge graph.

Companies

LeanScaleGTM Operations

Jake's firm, building AI-native revenue operations for fast-growing B2B SaaS companies — he is explicit that the output is working systems rather than decks about AI. LeanScale builds the context graph on Vasco.

00:55 · 05:46Company →

People

Jake Toepelnarrator

Chief Technology OfficerProfile →

Tools & software

VascoSemantic Layer

LeanScale's context graph, named as what the demonstrated system is built on — the semantic layer carrying definitions, motions and plan, and resolving records to a single identity across CRM, billing and product.

Website ↗
SalesforceCRM

Used as the identity-resolution example: billing says the account churned while Salesforce says it is wide open, and the agent has no basis for reconciling them.

Website ↗
ClaudeAI Assistant

Named alongside ChatGPT as the licence teams already hold and assume is sufficient. Jake's point is that distributing seats without a shared semantic layer produces fifty versions of the truth.

Website ↗
ChatGPTAI Assistant

Cited with Claude as the AI licence a team may already have, and the reason they assume they can run these plays immediately.

Website ↗
Methodologies referenced Evidence-based ICP re-derivationMessaging teardown against recorded callsLive five-whys pipeline diagnostic
Related LeanScale Services

Where this shows up in the work

AI for GTM

Agents and workflows that multiply operator throughput — the 'make humans superhuman' thesis, operationalized.

The episode is a direct demonstration of the AI-native GTM work — the agent plays and the context graph that makes their answers trustworthy.

Fractional RevOps

Embedded, field-facing RevOps leadership and execution — the operator model Jimmy describes, delivered as a service.

The four missing things are precisely the data foundation an embedded RevOps team builds before agents are deployed.

Forecasting & ARR Definition

Define a raise-worthy ARR and build a reliable consumption forecast across finance, data science, and go-to-market.

The live pipeline diagnostic and the pipeline-coverage failure both sit inside the forecasting process.

Frequently Asked Questions

Straight answers

Generated from the conversation, marked up for search and AI extraction.

What does AI-native GTM actually mean?

It means agents running the work that used to be non-humanly possible: the analysis nobody had time for, and the answers that used to take a data team three sprints, available on demand in plain English. The distinction Jake Toepel draws is against a chatbot bolted onto a CRM. The test is whether the agent produces analysis the team genuinely could not perform before, rather than automating something that was merely slow.

Why does AI give the wrong answer when pointed at a real CRM?

Four things are missing. Shared definitions — the model does not know that expansion pipeline and new business pipeline are different, so it blends them. Identity resolution — it cannot tell whether Acme Inc, Acme Co and Acme 123 are one company, or reconcile a billing system saying an account churned against a CRM saying it is open. The plan — the targets and quota to measure against. And memory — what an ICP means at this company and what happened last quarter. Without those, every answer is a confident guess.

What is a context graph?

A context graph is a semantic layer that sits beneath an AI agent and maps raw data to what it actually means in a specific business. It carries the company's definitions, motions and plan, and resolves every record to a single source of truth across CRM, billing and product systems. With it in place, an agent answers by reading off a model of the business the team has already agreed is correct, rather than inferring meaning from raw fields. LeanScale builds this on Vasco.

Why isn't buying AI licences for the whole team enough?

Because without a shared semantic layer underneath, every person defines pipeline their own way and the organisation ends up with as many versions of the truth as it has people asking questions. The licence gives access to a model, not to an agreed definition of the business. Building the foundation once is what makes the self-serve answers people were promised actually work across a team.

How do you find your real ICP using an agent?

Ask the uncomfortable version of the question: across every account closed in the last four quarters, cross-reference deal size, sales cycle and six-month retention, then identify which segments actually win and which ones the company keeps selling to and should not. Including retention matters — it separates a segment that closes easily from one that stays. In the demonstration the answer was mid-market with a strong technical champion, closing in half the time, rather than the enterprise logos on the website.

Why is a confident wrong answer worse than no answer at all?

Because of where it ends up. A number that arrives instantly and sounds certain carries no signal that it is unreliable, so it gets forwarded into a forecast call or a board deck before anyone questions it. Having no answer is visibly a gap and prompts someone to go and find out. The practical implication is that agent surfaces should decline to answer when required context is missing rather than approximating.

Timeline

Jump through the conversation

Click any chapter to open it in the transcript.

00:00The promise: three plays, then the part everyone skips 01:07What AI-native GTM actually means 01:31Play 1 — Who is actually your best customer? 02:09The answer: your real ICP isn't the logos on your website 02:25Play 2 — Is your messaging actually landing? 02:47Keep this line, kill that one — with receipts 03:12Play 3 — The live pipeline diagnostic 03:57Five whys of root cause before the call ends 04:16Now try it on your own CRM (it breaks) 04:43The four things missing: definitions, identity, plan, memory 05:46The context graph: the semantic layer underneath 06:23Why buying everyone a Claude license doesn't work 06:51Where to start, and what's in the next video
Full Transcript

The whole conversation

Broken into chapters, searchable, verbatim from the audio. Speakers inferred (not diarized).

Transcript as markdown ↓ Whole episode as markdown ↓
00:00The promise: three plays, then the part everyone skips

0:00 Right now, there's a good chance your CEO or your board has told you they want an AI-native go-to-market.

0:07 And there's an even better chance nobody's actually told you what that means.

0:11 So I'm going to show you.

0:13 In the next 10 minutes, you will watch one AI agent run three of the highest leverage plays in go-to-market.

0:19 Your ICP, your messaging, and a live pipeline diagnostic in less time than it takes most teams to book the meeting.

0:27 And then I'm going to show you the part that everybody skips because the second you try this on your own data, it all falls apart.

0:34 And the one thing that fixes it is a concept called the context graph.

0:38 It is the single most important part of AI-native GTM, and almost nobody is talking about it.

0:44 And we'll walk through how we're leveraging VASCO to build this.

0:48 So watch the use cases first, but stay to the end because that's where I break down the context graph, and that is the whole game.

0:55 I'm Jake, the CTO here at LeanScale, and we build AI-native revenue operations for some of the fastest-growing companies in B2B SaaS.

1:03 Not slide decks about AI, the actual working systems.

01:07What AI-native GTM actually means

1:07 So let me define the thing that everyone is chasing.

1:10 AI-native GTM just means this, agents running the work that used to be non-humanly possible.

1:16 The analysis nobody had time for, the answer that used to take your data team three sprints, running on-demand in plain English.

1:25 That is it. So let me stop describing it and just show you.

1:29 Three use cases. Here's the first.

01:31Play 1 — Who is actually your best customer?

1:31 Use case one. Who is actually your best customer?

1:35 Now, every company has an ICP slide.

1:37 It was written 18 months ago by somebody who does not work here anymore.

1:41 So I'm going to ask my agent the uncomfortable version of that question.

1:45 Let's look at every account we closed in the last four quarters, cross-reference deal size, sales cycle, and six-month retention.

1:52 Tell me which segments actually win and which ones we keep selling to that we should not.

1:57 Now, watch what it does here.

1:59 It's going to build itself a checklist, pull the accounts, segments them, and notice it's not just summarizing, it's hunting for the pattern.

2:08 And here's the answer.

02:09The answer: your real ICP isn't the logos on your website

2:09 Our real ICP is not the enterprise logos on the website.

2:13 It is mid-market with a strong technical champion closing in half the time at twice their attention.

2:19 That is a board level strategy conversation that used to take a quarter.

2:23 We just had it in 90 seconds.

02:25Play 2 — Is your messaging actually landing?

2:25 Use case number two. Is our messaging actually landing?

2:29 Marketing has positioning. The market has opinions.

2:32 Those are usually not the same document.

2:35 So I will hand the agent our last 20 sales calls and our current messaging and ask,

2:40 what are prospects actually responding to?

2:43 What falls flat and where are we just saying it wrong?

02:47Keep this line, kill that one — with receipts

2:47 And it comes back with receipts, real quotes from real calls, implementation in weeks, not months.

2:55 Keep it. That line closes deals.

2:57 One platform instead of five tools. Kill it.

3:01 Prospects hear that as a jack of all trades, master of none.

3:04 This is not a focus group you wait six weeks for.

3:07 That is your real voice of customer on demand every single week if you want it.

03:12Play 3 — The live pipeline diagnostic

3:12 Use case three. And this is the one that changes how you run the business, the live diagnostic.

3:18 Now, picture your Monday forecast call.

3:22 Pipeline is soft and nobody knows why.

3:24 Now, normally that question, why is pipeline down?

3:28 Goes to the data team and you get an answer three weeks later when half the quarter is already gone.

3:33 Let's watch what happens when the answer is live.

3:35 Pipeline is down quarter over quarter. Why is it concentrated in one region?

3:42 OK, which channel? Outbound specifically, well, why outbound?

3:51 Connect rates fell off a cliff six weeks ago, right when two reps ramped down.

03:57Five whys of root cause before the call ends

3:57 Five wise, a root cause in the meeting, the thing that used to take three sprints and a dashboard request

4:04 just happened before the call ended, and that is the real unlock speed.

4:09 Speed is the new moat and go to market constant recalibration instead of finding out you were wrong a quarter too late.

04:16Now try it on your own CRM (it breaks)

4:16 OK, if you have a Claude or a chat GPT license, you're probably thinking, just go do all this right now.

4:22 So let me save you three months of headache.

4:25 Watch what happens when you point raw A.I. at your real CRM and ask one simple question.

4:30 What is my pipeline coverage ratio?

4:33 Wow, it gave us a number instantly, confidently, and it's wrong.

4:38 Here's why. And this is the entire reason A.I. native is harder than the demo makes it look.

04:43The four things missing: definitions, identity, plan, memory

4:43 Four things are missing.

4:45 Number one, your shared definitions.

4:47 You have an expansion pipeline and a new business pipeline.

4:50 The A.I. doesn't know that, so it's blending them and it measures them against nothing because it doesn't have your plan.

4:56 Two is identity resolution.

4:59 Your billing system says this account churned. Salesforce says it is wide open.

5:04 Is Acme Inc, Acme Co, Acme 123 the same company?

5:08 The A.I. has no idea.

5:10 Three is your plan.

5:12 The targets, the quota and the actual number you measure against.

5:16 And four, memory.

5:19 What an ICP means for you?

5:21 What happened last quarter?

5:23 The context that makes the answers yours instead of just generic.

5:27 Without those four, every answer is a confident guess and a confident guess is worse than no answer because you're going to put that in front of the board.

5:36 So here is what we actually built.

5:38 This is the thing I told you to stay for at the start of this video.

5:41 And this is the part that makes everything you just saw trustworthy.

05:46The context graph: the semantic layer underneath

5:46 Underneath the agent sits what we call the context graph, a semantic layer that maps your raw data to what it actually means in your business.

5:55 Your definitions, your motions, your plan and resolves every record to a single source of truth across your CRM, your billing, your product.

6:04 This is what your go to market actually looks like underneath.

6:08 So when the agent answers now, it's not just guessing, it's reading off a model of your business that you have already agreed is correct.

6:17 That is the difference between a demo that wows you and a system you would bet the quarter on.

06:23Why buying everyone a Claude license doesn't work

6:23 It is also why just buying Claude licenses for the whole team doesn't work without this layer in the middle.

6:29 Everyone invents their own definition of pipeline.

6:31 Now you have 50 versions of the truth.

6:34 You build the foundation once and then everybody gets the self-serve answers they were promised in the first place.

6:40 So that is a native GM.

6:43 It's not a chatbot bolted onto your CRM.

6:46 It's agents running real plays on a foundation that makes the answers true.

06:51Where to start, and what's in the next video

6:51 If you're feeling the pressure to figure this out and most of you are, the place to start is not buying more tools.

6:57 It's an honest look at whether your data can even support this yet.

7:01 That's the assessment that we run.

7:03 Link is below.

7:04 We will show you exactly where you stand and what it takes to get to what you just watched.

7:08 In the next video, I'm going to show you how we run this across dozens of companies at once, the agency version and everything we have built on top of this layer.

7:18 I'm Jake from LeanScale and I'll see you there.

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